مجله کنترل
سال پانزدهم شماره 3 (پاییز 1400)

Traction control system (TCS) is one of the necessary systems for increasing vehicle safety. This control system is used to prevent excessive slipping of wheels especially when the vehicle suddenly starts to move. Keeping the wheels slip in a desirable range under unfavorable weather condition is a challenging issue due to unknown effects of road surface and severe nonlinear behavior of tire during the acceleration process. On the other hand, in designing a controller, the existence of some unknown uncertainties such as un-model dynamics and variation of vehicle parameters should be considered. Therefore, the presence of a nonlinear robust control law seems avoidable for TCS. In this paper, at first, using nonlinear predictive control method, a modern nonlinear optimal controller is designed for TCS. Then, unknown uncertainties of the system are adaptively estimated using a radial basis function neural network (RBFNN). Finally, some simulation results are presented for tracking the reference wheel slip in the presence of uncertainties for different maneuvers in order to assess the behavior of the proposed control system. The results show the effectiveness of the proposed control system against the nonlinear effects and uncertainties.

Recently, Carbon Nano (CN) structures are widely used in medical applications, especially the detection and treatment of cancer disease. Among various types of CNs, Carbone Nano Tubes (CNTs) attracted many researchers' attention to consider them toward clinical application. Regarding the intrinsic structure of CNTs, they can be used widely in drug delivery applications. Functionalized CNTs and conjugated with drug and magnetic nanoparticles (MNPs), represents an opportunity toward targeted drug delivery. In the mentioned system, MNPs play as a magnetic actuator, which can be externally excited. Delivery of the drug to a specific area, specifically inside the cellular membrane, is essential. To conduct a well-designed delivery system, the interaction force profile is needed to cross the CNTs through the membrane. The process is the primary point in a targeted drug delivery system. In this study, the computational analysis of crossing functionalized /CNTs containing anti-cancer drug through the cell membrane (lung cell) are investigated. The mathematical model shows the frequency behaviour of the cell membrane and provides a physical relation between crossing velocities and interaction forces.  In this paper, the result is based on a complex Molecular scale simulation in which they entirely compute the producer of drug delivery. The dynamics equation of the system is presented in the time and frequency domain, which can lean to provide an optimal external magnetic field profile. This design helps nanotechnologist to precisely analyze drug delivery dynamics during the time and how to implement in clinical applications. The results provide an optimal profile to deliver the drug and crossing through the cell membrane in 30 seconds, 1, 2 and 5 minutes.

Nowadays, industrial systems deal with a wide range of constraints. Output saturation and lack of system model are two types of these constraints. In this paper, a Data-Driven Adaptive Predictive Control (DDAPC) is propounded for a family of unknown non-linear systems featuring output saturation. The design of the control signal only dependent on the input and output data of the system. In this regard, a new adaptive predictive control scheme is suggested using the new developed dynamic linearization model. The stability analysis of the proposed method is provided by proving the boundedness of the tracking error for both time varying and constant desired reference signal and considering the output saturation data, which is a common physical constraint in industrial systems. Furthermore, the proposed method is more robust against the model uncertainties and nonlinearities, in comparison with the common model-based adaptive methods, since its controller design procedures as well as the stability analysis are independent of plant model. To verify the efficiency and applicability of the suggested approach some applicational and numerical simulation examples are provided.

This paper presents design and implementation of Model Based Predictive Controller (MPC) for a novel Bi-Rotor Moving Mass Controlled (MMC) Unmanned Aerial Vehicle (UAV). Due to the strict constrained control inputs in this type of UAV, it is necessary to take into account the constrained controller design and un-constrained control methods are not applicable. MPC controller which is designed based on the linear model by considering control constraints, is implemented on the nonlinear model of the UAV’s planar motion and compared with LQR controller, the simulation results show significant performance of this controller in control of the UAV while respecting control constraints.

In this paper, a new algorithm in order to increase the satisfaction of terminal constraints in the predictive control problem is presented. In this algorithm, by extracting mathematical relations, the terminal constraints are transferred from the final moment to the current moments. In each iteration, this transformation takes place in the optimization problem. At each moment of optimization, a new expression is obtained in terms of control inputs in each of the finite prediction control horizons. The equation of this transfer constraint is derived based on the variable discrete equations with the time of the controlled process and its execution at any moment fulfills the final constraints of the problem. This new algorithm, after extracting its mathematical relationships, is used to track the path of a robot with terminal constraints, and its performance is demonstrated using robot dynamics simulations. Also, the stability analysis of the proposed controller is performed by writing an appropriate cost function and applying Lyapunov theorem.

In this paper, the problem of fractional order multi-agent tracking control problem is considered. Uncertainties, error constraints, transient response suitability and desirable response tracking problems are the challenges in this study. Because of these problems and challenges, the  adaptive control and Neural Networks approximators approaches are used in this study. In the first part of this article, the fractional order multi-agent systems are investigated with unknown parameters in the presence of error constraints. Then, a controller is designed on the basis of adaptive control and dynamic surface control so that the control objective of pursuing the desired output is achieved in the presence of the required constraints. The effective performance of the proposed controller is demonstrated by simulation by MATLAB software.

A dynamic model in the d-q frame is introduced for wider coverage of the faults of three-phase induction motor. In this frame, it is possible to implement and analyze more accurately due to the evaluation of stator currents instead of rotor. In the proposed model, eccentricity and stator winding faults have been investigated. The results could cover more than 70% of the faults of the three-phase induction motor.